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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Research project. A Memory solution for users, teams, and applications.
| Date | Stars |
|---|---|
| 2026-07-24 | 2170 |
| 2026-07-25 | 2170 |
| 2026-07-28 | 2170 |
| 2026-07-30 | 2170 |
| 2026-08-06 | 2170 |
Today
— stars today
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Momentum
0.0
growth rate 0.00%/day
> [!CAUTION]
> This is an archived research project. The code serves as a learning resource, not production software. Use it with caution, and at your own risk. No support is provided.
Kernel Memory
=============
[](https://github.com/microsoft/kernel-memory/blob/main/LICENSE)
This repository presents best practices and a reference implementation for Memory in specific AI
and LLMs application scenarios. Please note that **the code provided serves as a demonstration**
and is **not an officially supported** Microsoft offering.
**Kernel Memory** (KM) is a **multi-modal [AI Service](service/Service/README.md)** specialized
in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support
for **[Retrieval Augmented Generation](https://en.wikipedia.org/wiki/Prompt_engineering#Retrieval-augmented_generation)**
(RAG), synthetic memory, prompt engineering, and custom semantic memory processing.
KM is available as a **Web Service**, as a **[Docker container](https://hub.docker.com/r/kernelmemory/service)**,
a **[Plugin](https://learn.microsoft.com/copilot/plugins/overview)** for ChatGPT/Copilot/Semantic
Kernel, and as a .NET library for embedded applications.
Utilizing advanced embeddings and LLMs, the system enables Natural Language querying for obtaining
answers from the indexed data, complete with citations and links to the original sources.
Kernel Memory is designed for seamless integration as a Plugin with [Semantic Kernel](https://github.com/microsoft/semantic-kernel),
Microsoft Copilot and ChatGPT.
Kernel Memory Service on Azure
==============================
Kernel Memory can be deployed in various configurations, including as a **Service** in Azure.
To learn more about deploying Kernel Memory in Azure, please refer to the
[Azure deployment guide](https://microsoft.github.io/kernel-memory/azure).
For detailed instructions on deploying to Azure, you can check the [infrastructure documentation](/infra/README.md).
If you are already familiar with these resources, you can quickly deploy by clicking the following
button.
[](https://aka.ms/KernelMemoryDeploy2Azure)
🔗 See also: [Kernel Memory via Docker](#kernel-memory-docker-image) and [Serverless Kernel Memory with Azure services example](examples/007-dotnet-serverless-azure).
## Running Kernel Memory with Aspire
Kernel Memory can be easily run and imported in other projects also via .NET Aspire. For example:
```csharp
var builder = DistributedApplication.CreateBuilder();
builder.AddContainer("kernel-memory", "kernelmemory/service")
.WithEnvironment("KernelMemory__TextGeneratorType", "OpenAI")
.WithEnvironment("KernelMemory__DataIngestion__EmbeddingGeneratorTypes__0", "OpenAI")
.WithEnvironment("KernelMemory__Retrieval__EmbeddingGeneratorType", "OpenAI")
.WithEnvironment("KernelMemory__Services__OpenAI__APIKey", "...your OpenAI key...");
builder.Build().Run();
```
[](examples/303-dotnet-aspire/Program.cs)
Data Ingestion using Kernel Memory OpenAPI Web Service
======================================================
The example show the default documents ingestion pipeline:
1. Extract text: automatically recognize the file format and extract the information
2. Partition the text in small chunks, ready for search and RAG prompts
3. Extract embeddings using any LLM embedding generator
4. Save embeddings into a vector index such as
[Azure AI Search](https://learn.microsoft.com/azure/search/vector-search-overview),
[Qdrant](https://qdrant.tech/) or other DBs.
The example shows how to **safeguard private information** specifying who owns each document, and
how to **organize data** for search and faceted navigation, using **Tags**.
## C#
> ```csharp
> #r "nuget: Microsoft.KernelMemory.WebExcerpt of 32,095 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9f952eee0c6d8d54, topic:rag, topic:memory, readme:retrieval-augmented generation
matched fp:9f952eee0c6d8d54, topic:semantic-search, readme:vector index, readme:vector search
matched fp:9f952eee0c6d8d54, topic:llm, readme:reference implementation